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International Journal of Biomedical Imaging
Volume 2014, Article ID 237648, 8 pages
Research Article

Active Contour Model Coupling with Higher Order Diffusion for Medical Image Segmentation

1College of Information Engineering, Qingdao University, Qingdao 266071, China
2College of Physics Science, Qingdao University, Qingdao 266071, China
3The Affiliated Hospital of Medical College, Qingdao University, Qingdao 266003, China

Received 23 November 2013; Revised 20 January 2014; Accepted 27 January 2014; Published 2 March 2014

Academic Editor: Guowei Wei

Copyright © 2014 Guodong Wang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Active contour models are very popular in image segmentation. Different features such as mean gray and variance are selected for different purpose. But for image with intensity inhomogeneities, there are no features for segmentation using the active contour model. The images with intensity inhomogeneities often occurred in real world especially in medical images. To deal with the difficulties raised in image segmentation with intensity inhomogeneities, a new active contour model with higher-order diffusion method is proposed. With the addition of gradient and Laplace information, the active contour model can converge to the edge of the image even with the intensity inhomogeneities. Because of the introduction of Laplace information, the difference scheme becomes more difficult. To enhance the efficiency of the segmentation, the fast Split Bregman algorithm is designed for the segmentation implementation. The performance of our method is demonstrated through numerical experiments of some medical image segmentations with intensity inhomogeneities.